Aurelio Cortese

1.1k total citations
28 papers, 544 citations indexed

About

Aurelio Cortese is a scholar working on Cognitive Neuroscience, Behavioral Neuroscience and Cellular and Molecular Neuroscience. According to data from OpenAlex, Aurelio Cortese has authored 28 papers receiving a total of 544 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Cognitive Neuroscience, 3 papers in Behavioral Neuroscience and 2 papers in Cellular and Molecular Neuroscience. Recurrent topics in Aurelio Cortese's work include Neural dynamics and brain function (16 papers), Functional Brain Connectivity Studies (10 papers) and Neural and Behavioral Psychology Studies (10 papers). Aurelio Cortese is often cited by papers focused on Neural dynamics and brain function (16 papers), Functional Brain Connectivity Studies (10 papers) and Neural and Behavioral Psychology Studies (10 papers). Aurelio Cortese collaborates with scholars based in Japan, United States and United Kingdom. Aurelio Cortese's co-authors include Mitsuo Kawato, Hakwan Lau, Ai Koizumi, Kaoru Amano, Vincent Taschereau‐Dumouchel, Kazuhisa Shibata, Benedetto De Martino, Toshinori Chiba, Takeo Watanabe and Wako Yoshida and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and NeuroImage.

In The Last Decade

Aurelio Cortese

24 papers receiving 540 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Aurelio Cortese Japan 12 451 70 45 38 36 28 544
Chantal Roggeman Belgium 12 479 1.1× 98 1.4× 51 1.1× 44 1.2× 27 0.8× 14 728
Saee Paliwal Switzerland 7 216 0.5× 84 1.2× 59 1.3× 50 1.3× 20 0.6× 8 447
Hong Yuan China 11 376 0.8× 117 1.7× 43 1.0× 37 1.0× 35 1.0× 25 477
Adam Krawitz United States 8 502 1.1× 179 2.6× 41 0.9× 38 1.0× 18 0.5× 9 626
Pegah Sarkheil Germany 14 513 1.1× 176 2.5× 66 1.5× 78 2.1× 45 1.3× 26 645
Taylor Salo United States 14 466 1.0× 169 2.4× 66 1.5× 77 2.0× 25 0.7× 39 666
Tyler Santander United States 14 265 0.6× 58 0.8× 92 2.0× 31 0.8× 29 0.8× 29 588
Aaron S. W. Wong Australia 9 404 0.9× 47 0.7× 24 0.5× 25 0.7× 39 1.1× 20 504
Inês Almeida Portugal 12 231 0.5× 93 1.3× 48 1.1× 83 2.2× 28 0.8× 21 408
Joshua de Souza Singapore 7 331 0.7× 51 0.7× 44 1.0× 11 0.3× 33 0.9× 8 431

Countries citing papers authored by Aurelio Cortese

Since Specialization
Citations

This map shows the geographic impact of Aurelio Cortese's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aurelio Cortese with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurelio Cortese more than expected).

Fields of papers citing papers by Aurelio Cortese

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aurelio Cortese. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aurelio Cortese. The network helps show where Aurelio Cortese may publish in the future.

Co-authorship network of co-authors of Aurelio Cortese

This figure shows the co-authorship network connecting the top 25 collaborators of Aurelio Cortese. A scholar is included among the top collaborators of Aurelio Cortese based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aurelio Cortese. Aurelio Cortese is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kubo, Takatomi, et al.. (2025). Blaming luck, claiming skill: Self-attribution bias in error assignment. PLoS Computational Biology. 21(12). e1013787–e1013787.
2.
Aellen, Florence M., et al.. (2024). Context changes retrieval of prospective outcomes during decision deliberation. Cerebral Cortex. 34(12).
3.
Matsumoto, Nobuyoshi, et al.. (2024). Dopamine-induced relaxation of spike synchrony diversifies burst patterns in cultured hippocampal networks. Neural Networks. 181. 106888–106888.
4.
Cortese, Aurelio, et al.. (2024). Time-dependent neural arbitration between cue associative and episodic fear memories. Nature Communications. 15(1). 8706–8706.
5.
Chiba, Toshinori, Kentaro Ide, Nao Kobayashi, et al.. (2023). Event-related PTSD symptoms as a high-risk factor for suicide: longitudinal observational study. Nature Mental Health. 1(12). 1013–1022. 2 indexed citations
6.
Martino, Benedetto De & Aurelio Cortese. (2022). Goals, usefulness and abstraction in value-based choice. Trends in Cognitive Sciences. 27(1). 65–80. 20 indexed citations
7.
Cortese, Aurelio. (2021). Metacognitive resources for adaptive learning⋆. Neuroscience Research. 178. 10–19. 15 indexed citations
8.
Kubo, Takatomi, Nao Kobayashi, Yuka Miyake, et al.. (2021). Multiple time measurements of multidimensional psychiatric states from immediately before the COVID-19 pandemic to one year later: a longitudinal online survey of the Japanese population. Translational Psychiatry. 11(1). 573–573. 10 indexed citations
9.
Cortese, Aurelio, et al.. (2021). Generalized attention-weighted reinforcement learning. Neural Networks. 145. 10–21. 11 indexed citations
10.
Cortese, Aurelio, Saori Tanaka, Kaoru Amano, et al.. (2021). The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments. Scientific Data. 8(1). 65–65. 12 indexed citations
11.
Lisi, Giuseppe, et al.. (2020). Brain network dynamics fingerprints are resilient to data heterogeneity. Journal of Neural Engineering. 18(2). 26004–26004. 4 indexed citations
12.
Cortese, Aurelio, Hakwan Lau, & Mitsuo Kawato. (2020). Unconscious reinforcement learning of hidden brain states supported by confidence. Nature Communications. 11(1). 4429–4429. 27 indexed citations
13.
Cortese, Aurelio, Benedetto De Martino, & Mitsuo Kawato. (2019). The neural and cognitive architecture for learning from a small sample. Current Opinion in Neurobiology. 55. 133–141. 21 indexed citations
14.
Taschereau‐Dumouchel, Vincent, et al.. (2018). Towards an unconscious neural reinforcement intervention for common fears. Proceedings of the National Academy of Sciences. 115(13). 3470–3475. 85 indexed citations
15.
Shibata, Kazuhisa, Giuseppe Lisi, Aurelio Cortese, et al.. (2018). Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. NeuroImage. 188. 539–556. 58 indexed citations
16.
Cortese, Aurelio, et al.. (2018). Identifying multivariate patterns for illusory color perception using decoded fMRI neurofeedback. Journal of Vision. 18(10). 872–872. 1 indexed citations
17.
Cortese, Aurelio, Kaoru Amano, Ai Koizumi, Hakwan Lau, & Mitsuo Kawato. (2017). Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants. NeuroImage. 149. 323–337. 32 indexed citations
18.
Koizumi, Ai, Aurelio Cortese, Kaoru Amano, Mitsuo Kawato, & Hakwan Lau. (2017). [Modulation of Metacognition with Decoded Neurofeedback].. PubMed. 69(12). 1427–1432. 1 indexed citations
19.
Cortese, Aurelio, Kaoru Amano, Ai Koizumi, Mitsuo Kawato, & Hakwan Lau. (2016). Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Nature Communications. 7(1). 13669–13669. 99 indexed citations
20.
Koizumi, Ai, Kaoru Amano, Aurelio Cortese, et al.. (2016). Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure. Nature Human Behaviour. 1(1). 87 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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